The aim of this project is to study and test various robotic techniques for disassembling used products, and techniques for reassembling the disassembled components to form new products. The reason and timeliness for this is that there is a strong focus on using sub-components of products to avoid unnecessary use of virgin materials. In this way, we can save valuable natural resources and speed up the green transition. There is already ongoing research into identifying how to manually disassemble products in the most efficient way, how to turn this into instructions for robotic disassembly, and how to identify to which degree any given product should be disassembled. However, there is little research on how to program the disassembly processes with robots. This is a problem as the whole approach of reuse will only be scalable if robotics and automation can be widely applied.
Compared to conventional automation, there are a range of new challenges for automating disassembly:
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The used products arrive in an unordered sequence.
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For the same type of product, there will be large variations because of different conditions of use (e.g. appearance variation due to dirt, dents and scratches; variation in forces needed for removal of fasteners etc.)
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Most disassembly processes have not hitherto been automated, and hence technologies for these need to be developed from scratch.
In this project, we will study how we can use our existing knowledge within model-based and sensor-guided adaptive control, programming by demonstration and machine learning to develop robot control methods that can be used for disassembly. We will select a range of relevant products for disassembly, and a subset of the most relevant and challenging disassembly processes. We will in the first phase design and implement solutions for virgin products, and in a second step show that we can adapt these solutions to account for the variations due to use mentioned above.
By the end of the project, we expect to have obtained knowledge that can be further developed and deployed by the Danish robotics industry and Danish manufacturing end users.
The project is funded by Fabrikant Vilhelm Pedersen og Hustrus Legat.
Contact:
Assistant Professor Iñigo Iturrate